In this paper, a new multimodal compression scheme is proposed with the aim of compressing jointly an image and a signal via a single codec. The key idea behind our approach is to insert a wavelet-decomposed signal into a decomposed image and then consider the mixture data as an image for compression with the Set Partitioning In Hierarchical Trees (SPIHT) encoder. The insertion stage is performed in detail wavelet sub-bands using a spiral insertion function. The evaluation process is assessed on both natural and medical images according to an objective and subjective comparison criteria. Moreover, four multimodal compression schemes are provided for the sake of fair assessment. Finally, experimental results demonstrate the effectiveness of the proposed approach to achieve significant gains in terms of Percentage of Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR) for both reconstructed signal and image.

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An improved multimodal signal-image compression scheme with application to natural images and biomedical data

We introduce in this paper an extension of the Multimodal Compression technique (MC) for the purpose of coding hyperspectral image sequences. The main idea requires few steps, namely: (1) reducing the size of the sequence by inserting smooth images containing less information into the remaining images of the same sequence, (2) then coding the new compacted sequence using 3D-SPIHT algorithm. In this new scheme, called MC-3D-SPIHT, the insertion is achieved only in the contour of each image, according to a non-supervised way, so that one can preserve the Region of Interest (ROI) quality. For this purpose, a mixing function is employed. After the decoding process, inserted images are extracted by a separation function and the original sequence is reconstructed. By considering data from AVIRIS database, we will show how one decrease significantly the computing time for both coding and decoding.

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Fast Encoding-Decoding of 3D Hyperspectral Images Using a Non-Supervised Multimodal Compression Scheme

In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel biosignals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only one codec instead of multiple codecs. Objectively, biosignal samples are merged in the spatial domain of the image using a specific mixing function. Afterwards, the whole mixture is compressed using JPEG 2000. The spatial mixing function inserts samples in low-frequency regions, defined using a set of operations, including down-sampling, interpolation, and quad-tree decomposition. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. The reconstruction quality is evaluated on a set of test data using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for the image and biosignals.

/pdf/multimodal-compression-applied-to-biomedical-data-1r7zeey6ux.pdf

Multimodal compression applied to biomedical data

In this paper, a wavelet-based multimodal compression method is proposed. The method jointly compresses a medical image and an ECG signal within a single codec, i.e., JPEG-2000 in an effective and simple way. The multimodal scheme operates in two main stages: the first stage, consists of the encoder and involves a mixing function, aiming at inserting the samples of the signal in the image according to a predefined insertion pattern in the wavelet domain. The second stage represented by a separation function, consists of the extraction process of the ECG signal from the image after performing the decoding stage. Both the cubic spline and the median edge detection (MED) predictor have been adopted to conduct the interpolation process for estimating image pixels. Intensive experiments have been conducted to evaluate the performance of the multimodal scheme using objective distortion criteria. Results show clear superiority of the proposed scheme over the conventional separate compression approach involving two codecs: JPEG-2000 for images and ECG SPIHT-1D as well as other competing multimodal compression schemes in terms of both PRD and SNR at the signal decompression stage while maintaining good image quality and exhibiting a reduced computational complexity. Improvements in terms of average PRD and SNR values are as significant as 0.7 and 6 dB at low bit rates and 0.06 and 2 dB at higher bit rates on a number of test ECG signals and medical images.

An efficient JPEG-2000 based multimodal compression scheme

This paper presents a new variant of multimodal compression scheme based on a spiral insertion function in the wavelet domain. The main idea consists of inserting a decomposed signal into a decomposed image prior to a joint SPIHT compression process. The insertion phase is achieved in the detail sub-bands of the wavelet decomposition. The evaluation is assessed on both natural and medical images using objective and subjective comparison criteria. The experimental results showed the effectiveness of the proposed approach to obtain significant gains in terms of reconstructed signal (PRD %) and image (PSNR).

A novel multimodal compression scheme based on a spiral insertion function in the wavelet domain

The aim of this chapter is to provide the reader with a new vision of compressing jointly medical images/videos and signals. This type of compression is called “multimodal compression”. The basic idea is that only one codec can be used to compress, at the same time, a combination of medical data (i.e. images, videos and signals). For instance, instead of using one codec for each signal or image/video which might complicate the software implementation, one should proceed as follows: for the encoding process, data are merged using various specific functions before being encoded using a given codec (e.g. JPEG, JPEG 2000 or H.264). For the decoding phase, data are first decoded and afterwards separated using an inverse merging-function. The performance of this approach in terms of compression-distortion appears promising.

Emre H. Zeybek

Papers

Introduction to Multimodal Compression of Biomedical Data

Multimodal compression applied to biomedical data